Clinicopathologic features, genomic profiles and outcomes of younger vs. older Chinese hormone receptor-positive (HR+)/HER2-negative (HER2-) metastatic breast cancer patients

Background Poor outcomes have been widely reported for younger vs. older breast cancer patients, but whether this is due to age itself or the enrichment of aggressive clinical features remains controversial. We have evaluated the clinicopathologic characteristics and genomic profiles of real-world hormone receptor-positive (HR+)/HER2-negative (HER2-) metastatic breast cancer (MBC) patients to examine the determinants of outcome for younger vs. older patients in a single clinical subtype undergoing treatment in the same clinic. Patients and methods This study included patients presenting at the Peking University Cancer Hospital with primary stage IV or first-line metastatic HR+/HER2- breast cancer who consented to an additional blood draw for genomic profiling prior to treatment. Plasma samples were analyzed with a targeted 152-gene NGS panel to assess somatic circulating tumor DNA (ctDNA) alterations. Genomic DNA (gDNA) extracted from peripheral blood mononuclear cells was analyzed for germline variants using a targeted 600-gene NGS panel. Kaplan-Meier survival analysis was performed to analyze disease free survival (DFS), progression free survival (PFS) and overall survival (OS) in association with clinicopathologic and genomic variables. Results Sixty-three patients presenting with HR+/HER2- MBC were enrolled in this study. Fourteen patients were < 40 years, 19 were 40-50 years, and 30 were > 50 years at the time of primary cancer diagnosis. No significant associations were observed between age and DFS, PFS or OS. Shorter OS was associated with de novo Stage IV disease (p = 0.002), Luminal B subtype (p = 0.006), high Ki67 index (p = 0.036), resistance to adjuvant endocrine therapy (p = 0.0001) and clinical stage (p = 0.015). Reduced OS was also observed in association with somatic alterations in FGFR1 (p = 0.008), CCND2 (p = 0.012), RB1 (p = 0.029) or TP53 (p = 0.029) genes, but not in association with germline variants. Conclusion In this group of real-world HR+/HER2- MBC breast cancer patients younger age was not associated with poor outcomes. While current guidelines recommend treatment decisions based on tumor biology rather than age, young HR+ breast cancer patients are more likely to receive chemotherapy. Our findings support the development of biomarker-driven treatment strategies for these patients.


Patients and study design.
Sixty-three HR+/HER2-metastatic breast cancer patients presenting at the time of metastatic relapse or with de-novo Stage IV metastatic disease at the Peking University Cancer Hospital from December 2015 -March 1919 who consented to an additional blood draw for genomic profiling participated in this study. Patient ages ranged from 27-82 years. Blood samples were prospectively collected from all patients, before any treatment was initiated in the metastatic setting.

Blood collection and cfDNA/gDNA extraction
Each single draw of 10 mL of whole blood was collected into a Streck tube before undergoing a twostep centrifugation to separate plasma and buffy coat compartments. Aliquoted samples were stored at -80°C for batch processing. DNA extraction, library preparation and sequencing were performed in a CAP-accredited laboratory (Huidu Shanghai). Circulating cell-free DNA (cfDNA) was extracted from plasma samples using the QIAamp circulating nucleic acid kit (Qiagen, Hilden, Germany). Quantity and quality of the purified cfDNA were checked using a Qubit fluorimeter (ThermoFisher Scientific, Waltham, Massachusetts, USA) and Bioanalyzer 2100 (Agilent Technologies, California, USA). For cfDNA samples with severe genomic contamination from peripheral blood cells, a bead-based size selection was performed to remove large genomic fragments (AMPure XP beads, Beckman Coulter, California, USA). Genomic DNA (gDNA) was extracted from matched peripheral blood mononuclear cells (PBMCs) using the QIAamp DNA Blood Mini Kit (Qiagen), then enzymatically fragmented and purified.

Library preparation, hybrid capture and sequencing
Five to 30 ng of extracted cfDNA or 30-50 ng of fragmented PBMC gDNA were then processed for library construction including end-repair dA-tailing and adapter ligation. Ligated library fragments with appropriate adapters were amplified via PCR. The amplified DNA libraries were then further checked using a Bioanalyzer 2100 and samples with sufficient yield were advanced to hybrid capture. Hybrid capture was conducted using Biotin labelled DNA probes. In brief, each library was hybridized overnight with a Predicine NGS panel and paramagnetic beads. The unbound fragments were washed away, and the enriched fragments were amplified via PCR amplification. The purified product was checked on a Bioanalyzer 2100 and then loaded into an Illumina NovaSeq 6000 (San Diego, CA, USA) for NGS sequencing with paired-end 2x150bp sequencing kits.

Analyses of NGS data from cfDNA
NGS data from cfDNA were analyzed using the Predicine DeepSea NGS analysis pipeline, which starts from the raw sequencing data (BCL files) and outputs the final mutation calls. Briefly, the pipeline first performed adapter trimming, barcode checking, and correction. Cleaned paired FASTQ files were aligned to human reference genome build hg19 using the BWA alignment tool. Consensus BAM files were then derived by merging paired-end reads originated from the same molecules (based on mapping location and unique molecular identifiers) as single strand fragments. Single strand fragments from the same double strand DNA molecules were further merged as double stranded. By using the error suppression method described in [Newman, 2016], both sequencing and PCR errors were mostly corrected during this process.

Somatic mutation identification
Candidate variants were called by comparing with local variant background (defined based on plasma samples from healthy donors and historical data). Variants were further filtered by log-odds (LOD) threshold [Cibulskis 2013], base and mapping quality thresholds, repeat regions and other quality metrics. Candidate somatic mutations were further filtered on the basis of gene annotation to identify those occurring in protein-coding regions. Intronic and silent changes were excluded, while mutations resulting in missense mutations, nonsense mutations, frameshifts, or splice site alterations were retained. Mutations annotated as benign or likely benign were also filtered out based on the ClinVar database [Landrum, 2016], or as common germline variants in databases including 1000 genomes [Auton, 205;Sudmant, 2015], ExAC [Lek, 2016], gnomAD (http://gnomad.broadinstitute.org) and KAVIAR [Glusman, 20111] with population allele frequency >0.5%. Finally, hematopoietic expansion-related variants that have been previously described, including those in DNMT3A, ASXL1, TET2, and specific alterations within ATM (residue 3008), GNAS (residue 201, 202), or JAK2 (residue 617) were marked as CHIP-related mutations.

Oncogenic Signaling Pathway Analysis
To examine the relative proportion of mutations within key oncogenic signaling pathways in this patient cohort, we filtered the list of genes included in a previous publication describing oncogenic signaling pathways [Sanchez-Vega, 2018] to include only those identified as breast cancer driver genes [Dietlein, 2020; Martinez-Jiminez, 2020]. The list of resulting genes is shown in the table below. The frequency of SNVs across these genes was compared across age groups and statistical significance was evaluated using the Fisher's Exact Test.

Germline DNA analysis
Germline variants were determined by concurrent sequencing of buffy coat PBMCs using the PredicineATLAS TM targeted 600-gene panel. Candidate variants with low base quality, mapping scores, and other poor-quality metrics were filtered. Candidate variants with an allelic frequency <5% or with less than 8 distinct reads containing the mutation were excluded. Unknown variants in repeat regions were also excluded. Details of the analytical workflow are provided above in "Analyses of NGS data generated from cfDNA".